About the project

In resource poor rural areas there is a lack of proper laboratory facilities and also a lack of clinical expertise to make accurate diagnoses. When healthcare workers in these rural settings are not able to rely on laboratory based diagnostics, there are usually two paths they take:

Those who do not have sufficient clincal experience will not know the clinical signs of TB infection and patients with TB will be left undiagnosed (according to the WHO, every year 3 million people with TB are left undiagnosed)

If the healthcare worker knows some clinical signs of TB and knows that there is a high incidenc in their area, they will prescribe TB medications to patients who have some symptoms that match TB; a significant number of these patients will not actually have TB and they end up receiving medications they do not need and which have side effects.

The TB LAM test is a first step in addressing the needs of those in resource poor settings because it is inexpensive and can be used at the point of care without the need for laboratory facilities. However, because of the interpretive difficulties with the test, up to 50% of TB positive patients are misdiagnosed as being TB negative. Our diagnostic strip reader app and data platform can address this problem and preliminary results indicate a much greater accuracy in being able to positively identify patients who have TB; thus helping to mitigate the problems of leaving TB patients undiagnosed and overuse of medication in patients who do not have TB. Improving diagnosis rates by just 1% could result in tens of thousands of lives being saved per year. We are using technology to help increase the diagnostic accuracy of TB LAM.

Our solution: The TB LAM diagnostic test strip only has moderate accuracy in identifying patients who have TB. One of the reasons for this is the inability of clinicians to visually detect a positive band on the TB LAM diagnostic test strip. Our solution to the problem of low diagnostic accuracy of TB LAM will be the first clinically validated diagnostic strip reader app (via the Wellcome Trust funded clinical trial detailed in question G) and data platform that uses a smartphone or tablet to provide an accurate and repeatable reading of TB LAM, generating a quantifiable reading in situations where an accurate diagnosis is difficult, which is particularly relevant in resource-poor settings where labs and costly TB tests are inaccessible. The app will work on any smartphone or tablet device with a camera, including cheap low-end devices prevalent in resource poor settings. We also plan to build a substantial part of our core image processing technology using Javascript and HTML5 meaning that it will be portable technology, compatible with any device with a camera, including PCs, smartphones and tablets using any modern operating system.